chain_sim_susc: Simulate a single chain using a branching process while...

In sbfnk/bpmodels: Simulating and Analysing Transmission Chain Statistics using Branching Process Models

Simulate a single chain using a branching process while accounting for depletion of susceptibles.

Description

Simulate a single chain using a branching process while accounting for depletion of susceptibles.

Usage

chain_sim_susc(
  offspring = c("pois", "nbinom"),
  mn_offspring,
  disp_offspring,
  serial,
  t0 = 0,
  tf = Inf,
  pop,
  initial_immune = 0
)

Arguments

offspring	offspring distribution: a character string corresponding to the R distribution function. Currently only "pois" & "nbinom" are supported. Internally truncated distributions are used to avoid infecting more people than susceptibles available.
mn_offspring	the average number of secondary cases for each case
disp_offspring	the dispersion coefficient (var/mean) of the number of secondary cases. Ignored if offspring == "pois". Must be > 1.
serial	the serial interval. A function that takes one parameter (n), the number of serial intervals to randomly sample. Value must be >= 0.
t0	start time
tf	end time
pop	the population
initial_immune	the number of initial immunes in the population

Details

This function has a couple of key differences with chain_sim: it can only simulate one chain at a time, it can only handle implemented offspring distributions ("pois" and "nbinom"), it always tracks and returns a data frame containing the entire tree, the maximal length of chains is limited with pop instead of infinite.

Value

a data frame with columns time, id (a unique ID for each individual element of the chain), ancestor (the ID of the ancestor of each element), and generation.

Author(s)

Flavio Finger

Examples

chain_sim_susc("pois", mn_offspring = 0.5, serial = function(x) 3, pop = 100)

sbfnk/bpmodels documentation built on Nov. 7, 2023, 5:16 a.m.